What’s Changed Recently

Traditional Workflow:

graph LR
    A[❓ Research<br/>Question] --> B[📚 Learn<br/>Programming]
    B --> C[💻 Write<br/>Code]
    C --> D[🐛 Debug<br/>Errors]
    D --> E[📊 Analyze<br/>Results]
    E --> F[🧠 Interpret<br/>Findings]
    
    classDef startNode fill:#1a2332,stroke:#4fc3f7,stroke-width:3px,color:#e3f2fd
    classDef difficultNode fill:#2d1b17,stroke:#ff9800,stroke-width:2px,color:#fff3e0
    classDef endNode fill:#1b2d1b,stroke:#4caf50,stroke-width:3px,color:#e8f5e8
    
    class A startNode
    class B,C,D difficultNode
    class E,F endNode

AI-Assisted Approach:

graph LR
    A[❓ Research<br/>Question] --> B[🤖 Prompt<br/>AI]
    B --> C[👀 Review<br/>Code]
    C --> D[▶️ Run<br/>Analysis]
    D --> E[📊 Interpret<br/>Results]
    E --> F[✅ Check<br/>Logic]
    
    classDef startNode fill:#1a2332,stroke:#4fc3f7,stroke-width:3px,color:#e3f2fd
    classDef easyNode fill:#1b2d1b,stroke:#4caf50,stroke-width:2px,color:#e8f5e8
    classDef endNode fill:#1a2332,stroke:#00bcd4,stroke-width:3px,color:#e3f2fd
    
    class A startNode
    class B,C,D easyNode
    class E,F endNode

Key Difference

Basic Variable Assignment

R stores information as objects. We create objects using the assignment operator <-:

# Store a number
electoral_votes_to_win <- 270
electoral_votes_to_win

[1] 270

# Store text
candidate_name <- "Biden"
candidate_name

[1] "Biden"

# Store the result of a calculation
margin_2020 <- (81283501 - 74223975) / 155507476
margin_2020

[1] 0.0453967

Variable Types

R automatically determines the type of data you’re storing:

• Numeric: Numbers for calculations (270, 3.14, -5.2)
  • Used for vote counts, percentages, ages, income data
• Character: Text enclosed in quotes ("Biden", "Democratic", "2024")
  • Used for names, party affiliations, state labels, survey responses
• Logical: True/false values (TRUE, FALSE)
  • Used for yes/no questions, whether conditions are met

# These are different!
number_value <- 270
number_value 

[1] 270

text_value <- "270"
text_value

[1] "270"

Creating Vectors

We often want to store multiple values in a single variable. For example, we might want to store the electoral votes for swing states in 2020. We can do this by creating a vector.

Vectors are one-dimensional collections of related values.

# Electoral votes for swing states in 2020
swing_states <- c("Pennsylvania", "Michigan", "Wisconsin",
                  "Arizona", "Georgia", "Nevada")

electoral_votes <- c(20, 16, 10, 11, 16, 6)

Understanding the c() Function

The c() function stands for “combine” or “concatenate”. It’s how we create vectors in R.

Key points about c():

• Combines multiple values into a single vector
• All values must be the same type (all numbers or all text)
• Separate values with commas
• Can combine single values or other vectors

# Combining individual values
approval_ratings <- c(45, 48, 42, 50, 47)

# Combining text values
parties <- c("Democratic", "Republican", "Independent")

# R converts everything to the same type
mixed <- c(1, 2, "three", 4)  # All become text!
mixed

[1] "1"     "2"     "three" "4"    

Adding to Existing Vectors

You can expand vectors by combining them with new values using c():

# Start with core swing states
key_states <- c("Pennsylvania", "Michigan", "Wisconsin")

# Add more states to our analysis
key_states <- c(key_states, "Arizona", "Georgia")
key_states

[1] "Pennsylvania" "Michigan"     "Wisconsin"    "Arizona"      "Georgia"     

# Add even more states
all_competitive <- c(key_states, "Nevada", "North Carolina")
all_competitive

[1] "Pennsylvania"   "Michigan"       "Wisconsin"      "Arizona"       
[5] "Georgia"        "Nevada"         "North Carolina"

# Combine multiple vectors
rust_belt <- c("Pennsylvania", "Michigan", "Wisconsin", "Ohio")
sun_belt <- c("Arizona", "Georgia", "Nevada", "North Carolina")
all_battlegrounds <- c(rust_belt, sun_belt)
all_battlegrounds

[1] "Pennsylvania"   "Michigan"       "Wisconsin"      "Ohio"          
[5] "Arizona"        "Georgia"        "Nevada"         "North Carolina"

Basic Vector Operations

How many swing states?

length(swing_states)

[1] 6

Total electoral votes in swing states?

sum(electoral_votes)

[1] 79

What is the maximum number of electoral votes in a swing state?

max(electoral_votes)

[1] 20

Mathematical Operations

Vectors make calculations across multiple values easy:

What if we want to increase the electoral votes in each swing state by 5%?

# If turnout increased by 5% in each state
new_turnout <- electoral_votes * 1.05

new_turnout

[1] 21.00 16.80 10.50 11.55 16.80  6.30

Basic Summary Functions

# Presidential approval ratings (simulated)
approval_ratings <- c(45, 42, 48, 51, 44, 47, 43, 49, 46, 50)

# Central tendency
mean(approval_ratings)    # Average approval

[1] 46.5

median(approval_ratings)  # Middle value

[1] 46.5

# Spread
range(approval_ratings)   # Min and max

[1] 42 51

length(approval_ratings)  # Number of observations

[1] 10

The round() Function

Make numbers readable:

# Calculate Biden's 2020 vote percentage
biden_percentage <- 81283501 / 155507476

# Round to 2 decimal places
round(biden_percentage, 2)

[1] 0.52

# Round mean approval to 1 decimal place
round(mean(approval_ratings), 1)

[1] 46.5

What Are Data Frames?

Data frames are like spreadsheets that store data in rows and columns.

Key features:

• Rows: Observations (countries, voters, elections)
• Columns: Variables (population, party, vote share)
• Names: Each column has a descriptive name

Setting Up: Why Tidyverse?

The tidyverse is a collection of R packages designed for data science that share a common philosophy and grammar.

Why use tidyverse over base R?

• Consistent syntax: Functions work similarly across packages
• Readable code: Operations read like English sentences
• Better error messages: More helpful when things go wrong
• Modern approach: Designed for contemporary data analysis workflows

First, we load the tidyverse package:

library(tidyverse)

Loading CSV Files

What is a CSV file?

CSV stands for “Comma-Separated Values” - it’s a simple text format where:

• Each row represents one observation
• Columns are separated by commas
• First row usually contains variable names

Other file formats: R can load Excel files (.xlsx), SPSS files (.sav), Stata files (.dta), and many others, but for this course, all our data will be in CSV format.

To load CSV files, we use read_csv(). This is a function from the tidyverse package that reads in a CSV file and returns a data frame.

# Load UN population data
UNpop <- read_csv("../../data/UNpop.csv")

Essential Exploration Functions

# Quick overview of structure (what you will send to AI to help you)
glimpse(UNpop)

Rows: 7
Columns: 2
$ year      <dbl> 1950, 1960, 1970, 1980, 1990, 2000, 2010
$ world.pop <dbl> 2525779, 3026003, 3691173, 4449049, 5320817, 6127700, 6916183

Understanding the Output

glimpse() shows:

• Number of rows and columns
• Column names and types
• First few values in each column

This gives you a complete picture of your data structure.

Understanding Data Types

When we use glimpse(), we see information about each column’s data type. Some common data types:

• <dbl> means “double precision floating point number” (a decimal number)
  • Examples: population counts, vote shares, income amounts
• <chr> means “character” (text data)
  • Used for text
  • Examples: candidate names, party affiliations, state names
• <int> means “integer” (whole numbers)
  • Examples: number of votes, year, district numbers
• <lgl> means “logical” (TRUE/FALSE values)
  • Examples: incumbent status, ballot measure results
• <date> means date values (a date)
  • Examples: election dates, survey dates

More Essential Exploration Functions: Head

The head() function shows the first few rows of your data frame, which is useful for:

• Seeing what your actual data looks like (not just the structure)
• Checking if data loaded correctly
• Understanding the format and content of each column
• Getting a quick preview before doing analysis

Note: There’s also a tail() function that shows the last few rows, which can be helpful for checking if your data is complete or seeing the most recent observations.

head(UNpop)

# A tibble: 6 × 2
   year world.pop
  <dbl>     <dbl>
1  1950   2525779
2  1960   3026003
3  1970   3691173
4  1980   4449049
5  1990   5320817
6  2000   6127700

More Essential Exploration Functions: Summary

The summary() function provides descriptive statistics for each column in your data frame:

• For numeric columns (like population counts):
  • Min.: Minimum value
  • 1st Qu.: First quartile (25th percentile)
  • Median: Middle value (50th percentile)
  • Mean: Average value
  • 3rd Qu.: Third quartile (75th percentile)
  • Max.: Maximum value

# Summary statistics
summary(UNpop)

      year        world.pop      
 Min.   :1950   Min.   :2525779  
 1st Qu.:1965   1st Qu.:3358588  
 Median :1980   Median :4449049  
 Mean   :1980   Mean   :4579529  
 3rd Qu.:1995   3rd Qu.:5724258  
 Max.   :2010   Max.   :6916183  

More Essential Exploration Functions: Names

The names() function shows the names of the columns in your data frame:

• Useful for:
  • Checking column names
  • Understanding variable labels

# Column names
names(UNpop)

[1] "year"      "world.pop"

The UNpop Dataset

Source: United Nations population estimates

Time period: 1950-2010 (10-year intervals)

Variables:

• year: Year of measurement
• world.pop: World population (in thousands)

Simple Calculations

Population growth over time

UNpop$world.pop[7] - UNpop$world.pop[1]  # Growth from 1950 to 2010

[1] 4390404

Average population across all years

# Average population across all years
mean(UNpop$world.pop)

[1] 4579529

Convert from thousands to billions

# Convert from thousands to billions
UNpop$world.pop / 1000000

[1] 2.525779 3.026003 3.691173 4.449049 5.320817 6.127700 6.916183

Effective AI Prompts

Create and Analyze

Run this code:

# Create the data frame
congressional_leaders <- data.frame(
  name = c("Pelosi", "Schumer", "McConnell", "McCarthy"),
  party = c("Democratic", "Democratic", "Republican", "Republican"), 
  chamber = c("House", "Senate", "Senate", "House"),
  years_served = c(36, 24, 38, 16),
  age = c(82, 72, 80, 58)
)

Your tasks:

1. Calculate average years of service
2. Find the oldest leader
3. Count Democrats vs Republicans
4. Which chamber has more experienced leaders on average?

With AI Assistance

Try this prompt:

What you should get back

Something like this:

When we run the code

congress_summary <- congressional_leaders %>%
  group_by(party, chamber) %>%
  summarise(
    n                = n(), 
    mean_years       = mean(years_served, na.rm = TRUE),
    median_years     = median(years_served, na.rm = TRUE),
    sd_years         = sd(years_served, na.rm = TRUE),
    min_years        = min(years_served, na.rm = TRUE),
    max_years        = max(years_served, na.rm = TRUE),
    .groups          = "drop"
  )

congress_summary

# A tibble: 4 × 8
  party      chamber     n mean_years median_years sd_years min_years max_years
  <chr>      <chr>   <int>      <dbl>        <dbl>    <dbl>     <dbl>     <dbl>
1 Democratic House       1         36           36       NA        36        36
2 Democratic Senate      1         24           24       NA        24        24
3 Republican House       1         16           16       NA        16        16
4 Republican Senate      1         38           38       NA        38        38

Error: Object Not Found

Error: object 'data' not found

Cause: Variable name misspelled or not created yet

Solution: Check spelling! Make sure you are using the correct case.

Error: Could Not Find Function

Error: could not find function "read_csv"

Cause: Package not loaded

Solution: Run library(tidyverse) first

Error: Wrong Data Type

The Problem: When data looks like numbers but is stored as text (character strings), mathematical functions won’t work.

# This won't work as expected
numbers <- c("1", "2", "3")
mean(numbers)  # Error!

# Instead:
numbers <- c(1, 2, 3)
mean(numbers)  # Works!

Variable Naming

Good names:

• electoral_votes
• swing_states
• approval_ratings

Poor names:

• x, data, stuff
• electoralVotes (inconsistent style)

A full script

# Load packages first
library(tidyverse)

# Load data
UNpop <- read_csv("../../data/UNpop.csv")

# Examine data
glimpse(UNpop)

Rows: 7
Columns: 2
$ year      <dbl> 1950, 1960, 1970, 1980, 1990, 2000, 2010
$ world.pop <dbl> 2525779, 3026003, 3691173, 4449049, 5320817, 6127700, 6916183

# Analyze
mean(UNpop$world.pop)

[1] 4579529

Comments

Comments are lines that start with # and are ignored by R. They are useful for:

• Explaining what the code does
• Adding notes to yourself
• Making the code more readable

# Calculate population growth rate

growth_summary <- UNpop %>%
  slice(c(1, 7)) %>%                              # pick rows 1 and 7
  summarise(
    growth_rate = (last(world.pop) - first(world.pop)) / first(world.pop) * 100
  )

growth_summary

# A tibble: 1 × 1
  growth_rate
        <dbl>
1        174.

In Our Next Class

Working with Real Data

• Loading and exploring real political datasets
• Master essential data manipulation: filter(), select(), arrange()
• Using the pipe operator %>% to chain operations
• Handling missing values (NA) properly

Key Concepts to Remember

• R stores information as objects
• Vectors hold multiple related values
• Data frames organize data in rows and columns
• read_csv() loads external data files
• AI helps with syntax; you provide the thinking